CDC Daily by State
davegoblue
2/20/2022
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v003, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download on the CDC website
- CDC hospital data are available for download on the healthdata.gov website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.1 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v001.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download data
- renMapper - mapping file for renaming of variables in the raw data file
- selfListMapper - mapping file for transformations by variable type
- fullListMapper - mapping file for transformations across variable types
- lstComboMapper - mapping file for elements to be combined by data type (most common is to combine NYC and NYS data to NY if the file provides them separately)
- uqMapper - mapping file for fields that should combine to be unique keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control total checks
- checkControlAggMapper - mapping file for numeric variables for control total checks
- checkSimilarityMapper - mapping file for similarity checks to perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total checks to perform
- perCapMapper - named vector that drives conversion from original field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example Code Processing
The function is run to download and process the latest CDC case, hospitalization, and death data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220220.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220220.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220220.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220206")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220206")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220206")$dfRaw$vax
)
cdc_daily_220220 <- readRunCDCDaily(thruLabel="Feb 18, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-01-30 new_deaths 796 539 257 0.38501873
## 2 2022-01-29 new_deaths 1394 1098 296 0.23756019
## 3 2022-01-23 new_deaths 868 709 159 0.20164870
## 4 2022-01-22 new_deaths 1176 1028 148 0.13430127
## 5 2022-01-16 new_deaths 807 747 60 0.07722008
## 6 2022-01-25 new_deaths 3445 3220 225 0.06751688
## 7 2022-01-24 new_deaths 2679 2505 174 0.06712963
## 8 2022-01-27 new_deaths 2757 2592 165 0.06169377
## 9 2022-01-17 new_deaths 1429 1350 79 0.05685498
## 10 2022-01-26 new_deaths 3023 2858 165 0.05611291
## 11 2022-01-29 new_cases 195076 173891 21185 0.11483412
## 12 2022-01-30 new_cases 138089 124992 13097 0.09956629
## 13 2022-01-31 new_cases 620416 661083 40667 0.06346786
## 14 2022-02-04 new_cases 272825 289747 16922 0.06015941
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 4003629 3992287 11342 0.002836948
## 2 AL tot_deaths 5972978 5963555 9423 0.001578850
## 3 NC tot_deaths 6808527 6798521 10006 0.001470708
## 4 FL tot_cases 1290393798 1286243847 4149951 0.003221214
## 5 MD tot_cases 232491171 231793719 697452 0.003004414
## 6 KY tot_cases 252489934 252077588 412346 0.001634453
## 7 FL new_deaths 68042 66007 2035 0.030362032
## 8 KY new_deaths 13402 13063 339 0.025618742
## 9 AL new_deaths 17741 17371 370 0.021075416
## 10 NC new_deaths 21278 21097 181 0.008542773
## 11 RI new_deaths 3358 3354 4 0.001191895
## 12 MD new_cases 984492 961805 22687 0.023312989
## 13 KY new_cases 1208554 1193647 14907 0.012411118
## 14 TN new_cases 1912511 1926401 13890 0.007236425
## 15 FL new_cases 5648704 5629602 19102 0.003387388
## 16 NC new_cases 2478266 2470242 8024 0.003242998
## 17 SC new_cases 1408611 1405271 3340 0.002373945
## 18 RI new_cases 348326 347901 425 0.001220866
## 19 PW new_cases 2498 2495 3 0.001201682
##
##
##
## Raw file for cdcDaily:
## Rows: 45,540
## Columns: 15
## $ date <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-0~
## $ state <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "~
## $ tot_cases <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 28~
## $ conf_cases <dbl> 135705, NA, 3685032, NA, NA, 1130917, NA, 176228, NA, 7~
## $ prob_cases <dbl> 27860, NA, 0, NA, NA, 0, NA, 103954, NA, 108997, 0, NA,~
## $ new_cases <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 5~
## $ pnew_case <dbl> 220, 0, 0, 0, 0, 0, 0, 559, 0, 443, 0, 0, 0, 0, NA, 0, ~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408~
## $ conf_death <dbl> NA, 3616, 62011, 9604, NA, 19306, NA, 4739, NA, 10976, ~
## $ prob_death <dbl> NA, 149, 0, 218, NA, 2030, NA, 1991, NA, 1432, NA, 416,~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, ~
## $ pnew_death <dbl> 0, 0, 0, 1, 0, 16, 0, 7, 0, 2, 0, 0, 0, 0, NA, 0, 0, 4,~
## $ created_at <chr> "12/02/2021 02:35:20 PM", "08/19/2020 12:00:00 AM", "06~
## $ consent_cases <chr> "Agree", "N/A", "Agree", "N/A", "Not agree", "Agree", "~
## $ consent_deaths <chr> "Not agree", "Agree", "Agree", "Agree", "Not agree", "A~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-02-05 inp 108309 114478 6169 0.05538025
## 2 2022-02-05 hosp_ped 3323 3585 262 0.07585408
## 3 2021-11-24 hosp_ped 1387 1306 81 0.06015596
## 4 2022-02-05 hosp_adult 104794 110893 6099 0.05655417
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NH hosp_ped 725 811 86 0.111979167
## 2 ME hosp_ped 1373 1431 58 0.041369472
## 3 WV hosp_ped 4435 4554 119 0.026476805
## 4 VT hosp_ped 348 357 9 0.025531915
## 5 AR hosp_ped 10602 10393 209 0.019909502
## 6 KS hosp_ped 3856 3929 73 0.018754014
## 7 SC hosp_ped 7275 7393 118 0.016089446
## 8 VI hosp_ped 81 80 1 0.012422360
## 9 MA hosp_ped 9296 9412 116 0.012401112
## 10 ID hosp_ped 3155 3120 35 0.011155378
## 11 KY hosp_ped 15228 15375 147 0.009606901
## 12 NJ hosp_ped 15981 15838 143 0.008988340
## 13 IN hosp_ped 14697 14787 90 0.006105006
## 14 UT hosp_ped 7026 6998 28 0.003993155
## 15 NV hosp_ped 3856 3871 15 0.003882490
## 16 ND hosp_ped 2898 2909 11 0.003788531
## 17 TN hosp_ped 17497 17563 66 0.003764974
## 18 AL hosp_ped 17263 17319 56 0.003238679
## 19 NC hosp_ped 23574 23649 75 0.003176418
## 20 OR hosp_ped 7333 7356 23 0.003131595
## 21 MO hosp_ped 31461 31363 98 0.003119827
## 22 MS hosp_ped 8953 8926 27 0.003020303
## 23 PA hosp_ped 43632 43509 123 0.002823011
## 24 GA hosp_ped 42185 42079 106 0.002515902
## 25 IA hosp_ped 6153 6168 15 0.002434867
## 26 HI hosp_ped 1909 1913 4 0.002093145
## 27 AZ hosp_ped 22800 22847 47 0.002059281
## 28 NE hosp_ped 6181 6170 11 0.001781232
## 29 WA hosp_ped 10469 10484 15 0.001431776
## 30 CO hosp_ped 17474 17499 25 0.001429674
## 31 WI hosp_ped 8578 8568 10 0.001166453
## 32 IL hosp_ped 35034 35073 39 0.001112585
## 33 OK hosp_ped 20546 20524 22 0.001071342
## 34 RI hosp_ped 2843 2846 3 0.001054667
## 35 PR hosp_ped 16962 16979 17 0.001001738
## 36 AK hosp_ped 1996 1998 2 0.001001502
##
##
##
## Raw file for cdcHosp:
## Rows: 38,675
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 27,992
## Columns: 82
## $ date <date> 2022-02-19, 2022-02-19, 2022-0~
## $ MMWR_week <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7~
## $ state <chr> "NC", "TN", "MN", "MI", "SD", "~
## $ Distributed <dbl> 20744900, 12186030, 11914970, 1~
## $ Distributed_Janssen <dbl> 916100, 503900, 500200, 926300,~
## $ Distributed_Moderna <dbl> 7813760, 4644240, 4216760, 7835~
## $ Distributed_Pfizer <dbl> 12015040, 7037890, 7198010, 111~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 197795, 178441, 211272, 199329,~
## $ Distributed_Per_100k_12Plus <dbl> 230870, 208823, 249367, 231608,~
## $ Distributed_Per_100k_18Plus <dbl> 253377, 229098, 274762, 253818,~
## $ Distributed_Per_100k_65Plus <dbl> 1184680, 1065780, 1294570, 1127~
## $ vxa <dbl> 16040239, 9551129, 9853584, 150~
## $ Administered_12Plus <dbl> 15576577, 9369683, 9460637, 146~
## $ Administered_18Plus <dbl> 14630091, 8914389, 8826086, 138~
## $ Administered_65Plus <dbl> 4239236, 2778123, 2487485, 4293~
## $ Administered_Janssen <dbl> 508845, 259901, 353693, 459665,~
## $ Administered_Moderna <dbl> 5969173, 3654211, 3581985, 5900~
## $ Administered_Pfizer <dbl> 9561290, 5583600, 5913885, 8724~
## $ Administered_Unk_Manuf <dbl> 931, 53417, 4021, 2106, 133, 32~
## $ Admin_Per_100k <dbl> 152938, 139858, 174720, 151062,~
## $ Admin_Per_100k_12Plus <dbl> 173352, 160562, 198000, 170666,~
## $ Admin_Per_100k_18Plus <dbl> 178691, 167591, 203531, 176626,~
## $ Admin_Per_100k_65Plus <dbl> 242091, 242972, 270267, 243193,~
## $ Recip_Administered <dbl> 15939232, 9383280, 9868373, 153~
## $ Administered_Dose1_Recip <dbl> 8596653, 4180275, 4183752, 6576~
## $ Administered_Dose1_Pop_Pct <dbl> 82.0, 61.2, 74.2, 65.9, 74.7, 0~
## $ Administered_Dose1_Recip_12Plus <dbl> 8331132, 4079234, 3968078, 6351~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 92.7, 69.9, 83.0, 73.9, 86.3, 0~
## $ Administered_Dose1_Recip_18Plus <dbl> 7823417, 3850407, 3680383, 5966~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 72.4, 84.9, 76.1, 88.9, 0~
## $ Administered_Dose1_Recip_65Plus <dbl> 2154949, 1047531, 937204, 16884~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 91.6, 95.0, 95.0, 95.0, 0~
## $ vxc <dbl> 6201249, 3646584, 3830382, 5889~
## $ vxcpoppct <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 0~
## $ Series_Complete_12Plus <dbl> 6011177, 3568185, 3651613, 5701~
## $ Series_Complete_12PlusPop_Pct <dbl> 66.9, 61.1, 76.4, 66.3, 69.0, 0~
## $ vxcgte18 <dbl> 5622542, 3375377, 3382210, 5355~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 0~
## $ vxcgte65 <dbl> 1498685, 956337, 876804, 153840~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 0~
## $ Series_Complete_Janssen <dbl> 477185, 232189, 326034, 416641,~
## $ Series_Complete_Moderna <dbl> 2152155, 1299423, 1287594, 2139~
## $ Series_Complete_Pfizer <dbl> 3571765, 2103546, 2215307, 3333~
## $ Series_Complete_Unk_Manuf <dbl> 144, 11426, 1447, 1082, 34, 0, ~
## $ Series_Complete_Janssen_12Plus <dbl> 477158, 232135, 326016, 416612,~
## $ Series_Complete_Moderna_12Plus <dbl> 2152040, 1299371, 1287540, 2138~
## $ Series_Complete_Pfizer_12Plus <dbl> 3381836, 2025317, 2036626, 3144~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 143, 11362, 1431, 1073, 34, 0, ~
## $ Series_Complete_Janssen_18Plus <dbl> 475728, 231891, 325496, 416312,~
## $ Series_Complete_Moderna_18Plus <dbl> 2149019, 1298802, 1285260, 2138~
## $ Series_Complete_Pfizer_18Plus <dbl> 2997656, 1833427, 1770066, 2799~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 139, 11257, 1388, 986, 34, 0, 5~
## $ Series_Complete_Janssen_65Plus <dbl> 54321, 35691, 50477, 70861, 498~
## $ Series_Complete_Moderna_65Plus <dbl> 720300, 474871, 369087, 768663,~
## $ Series_Complete_Pfizer_65Plus <dbl> 723999, 439839, 456889, 698286,~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 65, 5936, 351, 592, 21, 0, 2511~
## $ Additional_Doses <dbl> 1544360, 1529958, 2125396, 2985~
## $ Additional_Doses_Vax_Pct <dbl> 24.9, 42.0, 55.5, 50.7, 39.8, 2~
## $ Additional_Doses_12Plus <dbl> 1544252, 1529687, 2125156, 2985~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 25.7, 42.9, 58.2, 52.4, 41.2, 2~
## $ Additional_Doses_18Plus <dbl> 1500845, 1502838, 2049913, 2906~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 26.7, 44.5, 60.6, 54.3, 43.1, 2~
## $ Additional_Doses_50Plus <dbl> 1017165, 1059552, 1281534, 1976~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 33.8, 56.3, 72.6, 64.9, 54.8, 4~
## $ Additional_Doses_65Plus <dbl> 578981, 632802, 708477, 1135879~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 38.6, 66.2, 80.8, 73.8, 62.9, 5~
## $ Additional_Doses_Moderna <dbl> 680325, 649042, 857058, 1316220~
## $ Additional_Doses_Pfizer <dbl> 836934, 856740, 1239887, 162309~
## $ Additional_Doses_Janssen <dbl> 27082, 20983, 28141, 46218, 254~
## $ Additional_Doses_Unk_Manuf <dbl> 19, 3193, 310, 106, 9, 22, 648,~
## $ Administered_Dose1_Recip_5Plus <dbl> 8594663, 4179589, 4181728, 6576~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 87.0, 65.1, 79.1, 69.8, 80.3, 0~
## $ Series_Complete_5Plus <dbl> 6200658, 3646444, 3829701, 5889~
## $ Series_Complete_5PlusPop_Pct <dbl> 62.8, 56.8, 72.4, 62.5, 64.1, 0~
## $ Administered_5Plus <dbl> 16037693, 9550281, 9850893, 150~
## $ Admin_Per_100k_5Plus <dbl> 162353, 148745, 186287, 160139,~
## $ Distributed_Per_100k_5Plus <dbl> 210004, 189797, 225320, 211315,~
## $ Series_Complete_Moderna_5Plus <dbl> 2152112, 1299406, 1287586, 2138~
## $ Series_Complete_Pfizer_5Plus <dbl> 3571235, 2103464, 2214653, 3333~
## $ Series_Complete_Janssen_5Plus <dbl> 477168, 232150, 326019, 416627,~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 143, 11424, 1443, 1081, 34, 0, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.84e+10 3.17e+8 7.79e+7 910251 44781
## 2 after 1.83e+10 3.15e+8 7.73e+7 905741 38709
## 3 pctchg 4.83e- 3 4.32e-3 6.93e-3 0.00495 0.136
##
##
## Processed for cdcDaily:
## Rows: 38,709
## Columns: 6
## $ date <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-05-13~
## $ state <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "NC",~
## $ tot_cases <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 280182~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408, 55~
## $ new_cases <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 537, ~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, 11, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.51e+7 3.88e+7 945228 38675
## 2 after 4.49e+7 3.86e+7 927959 37083
## 3 pctchg 4.84e-3 4.63e-3 0.0183 0.0412
##
##
## Processed for cdcHosp:
## Rows: 37,083
## Columns: 5
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state <chr> "HI", "NE", "IA", "NH", "HI", "DC", "KS", "NM", "ME", "NE",~
## $ inp <dbl> 111, 376, 497, 45, 110, 166, 474, 189, 23, 316, 546, 3246, ~
## $ hosp_adult <dbl> 111, 367, 487, 44, 108, 149, 454, 186, 23, 315, 534, 3104, ~
## $ hosp_ped <dbl> 0, 9, 10, 1, 2, 17, 5, 3, 0, 6, 12, 55, 8, 0, 1, 8, 2, 8, 6~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.66e+11 1.13e+11 1003870. 3.03e+10 1559557. 1.06e+11 1202494.
## 2 after 1.28e+11 5.46e+10 843159. 1.46e+10 1396120. 5.14e+10 1020516.
## 3 pctchg 5.20e- 1 5.16e- 1 0.160 5.16e- 1 0.105 5.17e- 1 0.151
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 22,083
## Columns: 9
## $ date <date> 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-1~
## $ state <chr> "NC", "TN", "MN", "MI", "SD", "OH", "MT", "WV", "VA", "IA"~
## $ vxa <dbl> 16040239, 9551129, 9853584, 15086338, 1349798, 17152418, 1~
## $ vxc <dbl> 6201249, 3646584, 3830382, 5889772, 527824, 6712161, 59625~
## $ vxcpoppct <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 57.4, 55.8, 56.6, 71.7, 60.9~
## $ vxcgte65 <dbl> 1498685, 956337, 876804, 1538402, 140420, 1779459, 175563,~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 87.0, 85.0, 83.5, 91.2, 92.0~
## $ vxcgte18 <dbl> 5622542, 3375377, 3382210, 5355218, 476217, 6118597, 54652~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 67.2, 65.0, 65.8, 81.0, 71.7~
##
## Integrated per capita data file:
## Rows: 38,973
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220220, ovrWriteError=FALSE)The latest hospital data are downloaded:
# Run for latest data, save as RDS
indivHosp_20220221 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220221.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 399,863
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 7503
## 2 Critical Access Hospitals 106952
## 3 Long Term 27474
## 4 Short Term 257934
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 25
## 2 GU 160
## 3 MP 80
## 4 PR 4400
## 5 VI 160
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 11667 387469 727 399863
## 2 all_adult_hospital_inpatient_bed_occupi~ 3328 364400 32135 399863
## 3 icu_beds_used_7_day_avg 1649 350757 47457 399863
## 4 inpatient_beds_7_day_avg 1730 396567 1566 399863
## 5 staffed_icu_adult_patients_confirmed_an~ 4251 279744 115868 399863
## 6 total_adult_patients_hospitalized_confi~ 2372 278715 118776 399863
## 7 total_beds_7_day_avg 6632 392858 373 399863
## 8 total_icu_beds_7_day_avg 2064 377884 19915 399863## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220221, ovrWriteError=FALSE)The post-processing capabilities are included:
# Create pivoted burden data
burdenPivotList_220220 <- postProcessCDCDaily(cdc_daily_220220,
dataThruLabel="Jan 2022",
keyDatesBurden=c("2022-01-31", "2021-07-31",
"2021-01-31", "2020-07-31"
),
keyDatesVaccine=c("2021-12-31", "2021-09-30",
"2021-06-30", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
The hospital summaries are also added:
# Can be run only as-needed
dfStateAgeBucket2019 <- readPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv") %>%
filterPopStateAge(keyCol="POPEST2019_CIV", keyColName="pop2019") %>%
bucketPopStateAge(popVar="pop2019")##
## -- Column specification --------------------------------------------------------
## cols(
## SUMLEV = col_character(),
## REGION = col_double(),
## DIVISION = col_double(),
## STATE = col_double(),
## NAME = col_character(),
## SEX = col_double(),
## AGE = col_double(),
## ESTBASE2010_CIV = col_double(),
## POPEST2010_CIV = col_double(),
## POPEST2011_CIV = col_double(),
## POPEST2012_CIV = col_double(),
## POPEST2013_CIV = col_double(),
## POPEST2014_CIV = col_double(),
## POPEST2015_CIV = col_double(),
## POPEST2016_CIV = col_double(),
## POPEST2017_CIV = col_double(),
## POPEST2018_CIV = col_double(),
## POPEST2019_CIV = col_double()
## )##
## *** File has been checked for uniqueness by: NAME SEX AGE
##
## [1] TRUE
## [1] TRUE
## [1] TRUE
##
## PASSED CHECK: United States total is the sum of states and DC
##
##
## PASSED CHECK: Age 999 total is the sum of the ages
##
##
## PASSED CHECK: Sex 0 total is the sum of the sexes
# Create hospitalized per capita data
hospPerCap_220220 <- hospAgePerCapita(dfStateAgeBucket2019,
lst=burdenPivotList_220220,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
The one-page CFR plot capability is included:
# Create CFR plots for select states
cfrStates <- list("FL"=list(keyState="FL", minDate="2020-08-01", multDeath=70),
"LA"=list(keyState="LA", minDate="2020-08-01", multDeath=80),
"CA"=list(keyState="CA", minDate="2020-08-01", multDeath=100),
"IL"=list(keyState="IL", minDate="2020-08-01", multDeath=100)
)
purrr::walk(cfrStates, .f=function(x) onePageCFRPlot(burdenPivotList_220220$dfPivot,
keyState=x$keyState,
minDate=x$minDate,
multDeath=x$multDeath
)
)
The peaks and valleys plots are included:
# Burden data
cdc_daily_220220$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"="Rolling 7-day mean deaths",
"new_cases"="Rolling 7-day mean cases (000)",
"inp"="Rolling 7-day mean in hospital (000)"
)
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## # A tibble: 3,107 x 11
## date regn new_deaths new_cases inp new_deaths_isPe~ new_cases_isPeak
## <date> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
## 1 2020-01-01 Nort~ NA NA NA FALSE FALSE
## 2 2020-01-01 South NA NA NA FALSE FALSE
## 3 2020-01-01 West NA NA NA FALSE FALSE
## 4 2020-01-02 Nort~ NA NA NA FALSE FALSE
## 5 2020-01-02 South NA NA NA FALSE FALSE
## 6 2020-01-02 West NA NA NA FALSE FALSE
## 7 2020-01-03 Nort~ NA NA NA FALSE FALSE
## 8 2020-01-03 South NA NA NA FALSE FALSE
## 9 2020-01-03 West NA NA NA FALSE FALSE
## 10 2020-01-04 Nort~ 0 0 0 FALSE FALSE
## # ... with 3,097 more rows, and 4 more variables: inp_isPeak <lgl>,
## # new_deaths_isValley <lgl>, new_cases_isValley <lgl>, inp_isValley <lgl># Vaccinations data for states with 8+ million population
cdc_daily_220220$dfPerCapita %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(date, state, vxa, vxc) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= "2020-12-01") %>%
makePeakValley(numVar=c("vxc", "vxa"),
windowWidth = 29,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"="Vaccines adminsitered (US)",
"vxc"="Became fully vaccinated (US)"
),
yLab=c("vxa"="Rolling 21-day mean administered (000)",
"vxc"="Rolling 21-day mean completed (000)"
)
)## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 5,364 x 8
## date state vxc vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # ... with 5,354 more rowsThe hospital utlization plots are included:
indivHosp_20220221 %>%
filter(state %in% c(state.abb, "DC"),
collection_week==max(collection_week)
) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20220221, keyHosp=., plotTitle="US Hospitals Summed")
